Joshua Hyman

The Missing Manual: Using National Student Clearinghouse Data to Track Postsecondary Outcomes.

This article explores the promises and pitfalls of using National Student Clearinghouse (NSC) data to measure a variety of postsecondary outcomes. We first describe the history of the NSC, the basic structure of its data, and recent research interest in using NSC data. Second, using information from the Integrated Postsecondary Education Data System (IPEDS), we calculate enrollment coverage rates for NSC data over time, by state, institution type, and demographic student subgroups. We find that coverage is highest among public institutions and lowest (but growing) among for-profit colleges. Across students, enrollment coverage is lower for minorities but similar for males and females. We also explore two potentially less salient sources of non-coverage: suppressed student records due to privacy laws and matching errors due to typographic inaccuracies in student names. To illustrate how this collection of measurement errors may affect estimates of the levels and gaps in postsecondary attendance and persistence, we perform several case-study analyses using administrative transcript data from Michigan public colleges. We close with a discussion of practical issues for program evaluators using NSC data.

ACT for All: The Effect of Mandatory College Entrance Exams on Postsecondary Attainment and Choice.

This paper examines the effects of requiring and paying for all public high school students to take a college entrance exam, a policy adopted by eleven states since 2001. I show that prior to the policy, for every ten poor students who score college-ready on the ACT or SAT, there are an additional five poor students who would score college-ready but who take neither exam. I use a difference-in-differences strategy to estimate the effects of the policy on postsecondary attainment and find small increases in enrollment at four-year institutions. The effects are concentrated among students less likely to take a college entrance exam in the absence of the policy and students in the poorest high schools. The students induced by the policy to enroll persist through college at approximately the same rate as their inframarginal peers. I calculate that the policy is more cost-effective than traditional student aid at boosting postsecondary attainment.

The Relative Returns to Education, Experience, and Attractiveness for Young Workers

We conduct a randomized résumé audit study, simultaneously examining the returns to education, experience, and physical attractiveness among young workers applying for entry-level, formal sector jobs in a developing country context. Employers do not value postsecondary education without a degree. Postsecondary vocational training increases the likelihood of a callback but only for blue-collar occupations typically offered only to male workers. Work experience is valued across most occupations; however, among service-sector jobs with in-person customer interactions, attractive applicants receive 23% more callbacks, swamping the returns to experience. Our results can help young workers make optimal choices to ease their school-to-work transition and guide policy makers in the design of labor market programs to ensure youth have the skills and qualifications that employers demand.

Data vs. Methods: Quasi-Experimental Evaluation of Alternative Sample Selection Corrections for Missing College Entrance Exam Score Data

In 2007, Michigan began requiring all high school students to take the ACT college entrance exam. This natural experiment allows us to evaluate the performance of several parametric and semiparametric sample selection correction models. We apply each model to the censored, prepolicy test score data and compare the predicted values to the uncensored, post-policy distribution. We vary the set of model predictors to imitate the varying levels of data detail to which a researcher may have access. We find that predictive performance is sensitive to predictor choice but not correction model choice. All models perform poorly using student demographics and school- and district-level characteristics as predictors. However, all models perform well when including students’ prior and contemporaneous scores on other tests. Similarly, correction models using group-level data perform better with more finely disaggregated groups, but produce similar predictions under different functional form assumptions. Our findings are not explained by an absence of selection, the assumptions of the parametric models holding, or the data lacking sufficient variation to permit useful semiparametric estimation. We conclude that “data beat methods” in this setting: gains from using less restrictive econometric methods are small relative to gains from seeking richer or more disaggregated data.